This document discusses parsing and analyzing large web server log files at scale. It summarizes that log files are usually huge in size and cannot be loaded entirely into memory. It proposes sequentially parsing chunks of lines and saving them to an efficient file format like Parquet to combine the files. This allows faster writing, reading and ingestion times compared to the raw log file format. Specific Python libraries like Pandas, Apache Arrow and Apache Parquet are used to efficiently convert and store the log data. A logs_to_df function is also defined that parses common/combined log formats line by line and saves chunks as Parquet files for scalable analysis of large log datasets.